Publications

Duan, ZX; Yang, YJ; Zhou, SH; Gao, ZQ; Zong, L; Fan, SH; Yin, J (2021). Estimating Gross Primary Productivity (GPP) over Rice-Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product. REMOTE SENSING, 13(21), 4229.

Abstract
Despite advances in remote sensing-based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP(MOD)) is less well understood over rice-wheat-rotation cropland. To improve the performance of GPP(MOD), a random forest (RF) machine learning model was constructed and employed over the rice-wheat double-cropping fields of eastern China. The RF-derived GPP (GPP(RF)) agreed well with the eddy covariance (EC)-derived GPP (GPP(EC)), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m(-2) d(-1). Therefore, it was deemed reliable to upscale GPP(EC) to regional scales through the RF model. The upscaled cumulative seasonal GPP(RF) was higher for rice (924 g C m(-2)) than that for wheat (532 g C m(-2)). By comparing GPP(MOD) and GPP(EC), we found that GPP(MOD) performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPP(MOD) was calibrated by GPP(RF), and the error range of GPP(MOD) (GPP(RF) minus GPP(MOD)) was found to be 2.5-3.25 g C m(-2) d(-1) for rice and 0.75-1.25 g C m(-2) d(-1) for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

DOI:
10.3390/rs13214229

ISSN: